Knowledge-constrained projection of high-dimensional data

Projection of high-dimensional data is usually done by reducing dimensionality of the data and transforming the data to the latent space. We created synthetic data to simulate real gene-expression datasets and we tested methods on both synthetic and real data. With this work we address the visualization of our data through implementation of regularized singular value decomposition (SVD) for biclustering using L0-norm and L1-norm. Additional knowledge is introduced to the model through regularization with the two prior adjacency matrices. We show that L0-norm SVD and L1-norm SVD give better results than standard SVD